Max Robotics — Global Service Robot Directory + US Certification

About

Mike Zheng Shou is a pioneering researcher at the intersection of computer vision, robotics, and human-computer interaction, with a particular focus on egocentric perception and embodied AI. His work addresses one of the most compelling challenges in modern robotics: enabling intelligent systems to learn from human demonstrations and translate that knowledge into actionable, real-world behavior. Shou's most influential contributions center on affordance grounding — teaching machines to understand where and how humans interact with objects. His 2023 paper on "Affordance Grounding from Demonstration Video to Target Image" (22 citations) exemplifies this vision, bridging the gap between observational learning and practical robot assistance. Complementing this, his AssistQ framework (15 citations) pioneered affordance-centric, question-driven task completion for egocentric assistants, laying groundwork for next-generation AR and wearable intelligence. Beyond affordance understanding, Shou has advanced multiview eye-gaze analysis through GazeVQA and tackled the formidable challenge of deformable object manipulation via DeformGS, demonstrating impressive breadth across perception and physical interaction domains. His cumulative body of work positions him as a key contributor shaping how robots and AI assistants learn to understand and assist humans in complex, real-world environments.